Lactation curves were modeled using a multiphasic function and effects of breed, parity, season of kidding, and level of milk production on the curve were studied. Milk production data on dairy goats were grouped into 90 subclasses: breed (5) by parity (3) by season (2) by level of production (3). Subclass means for three-day groups were smoothed and used to estimate parameters of a diphasic function, sum of two phases. For each phase, characteristics for scale (initial, peak, and 305-d yields) and for shape (time of peak and duration), which are functions of parameters, were analyzed using a linear model including breed, parity, season, and mean level of production as a covariate. Breed had little effect on scale or shape on the curve. Parity affected scale and shape characteristics of second phase of lactation primarily. Season had the most consistent effect on the curve, affecting scale and shape characteristics of each phase. Level of production affected scale and shape characteristics of second phase more than those of first phase. First phase, with its proximity to overall peak and short duration, could be interpreted as a "peak" phase. Second phase, affected largely by parity, could be interpreted as a "persistency" phase.Lactation curves were simulated using a multiphasic function and effects of smoothing on estimation of parameters for individual curves were studied. Curves were simulated for weekly and monthly observations over a 305-d lactation, for first and second-or-greater parity, and for low, medium, and high error. Two approaches to smoothing by LOWESS were investigated: individual smoothing using individual $f$'s computed from individual curves and global smoothing using a single $f$ computed as a mean of $f$'s, where $f$ is the proportion of data used in smoothing. Parameters of a diphasic function were estimated from non-smoothed, individually smoothed, and globally smoothed data. Individual and global smoothing produced a higher percentage of estimates within the parameter space and also estimates with smaller bias, sampling variance, and mean squared error compared with non-smoothed data. Global smoothing was preferred over individual smoothing because of ease of computing.